The Role of Coopetition in Fostering Innovation and Growth in New Technology-based Firms: A Game Theory Approach
Bibliographic record
Abstract
Objective: New technology-based firms (NTBFs) are key actors in creating value through innovation, but they face significant challenges in the rapidly changing and competitive technological environment. Methods: This research is a multi-method analysis aiming to present a model of relationships among drivers for collaboration and competition among technology-based companies and identify effective actions and policies to enhance coopetition (cooperation and competition) that can boost the ability of NTBFs to grow and commercialize innovations. The methodology of this study is exploratory in nature. Thus, it employs literature review method for gathering qualitative data, Fuzzy Delphi method for collecting data from experts, and DEMATEL-ISM method for modeling the relationships among drivers and demonstrating the impact of coopetition on the performance of NTBFs. Results: The research findings show that coopetition can improve growth, innovation, and commercialization in NTBFs by overcoming technological and competitive limitations. Conclusions: The study offers practical and social implications for managers, policy makers, and economic development by highlighting the role of coopetition in fostering innovation and prosperity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".